ACF and PACF¶

Run ACF and PACF analysis to select the right amount of lag

1. Imports¶

1.1 Packages¶

In [1]:
import datetime
import os
import pandas as pd
import sys

import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px

import statsmodels.api as sm
In [2]:
sys.path.append("../src/")
from prepare_data import set_type, split_train_test, check_split
from feature_engineering import extract_date_features, add_lockdown_periods, add_holidays_period, add_lags_sma

1.2 Options¶

In [3]:
%load_ext autoreload
%autoreload 2
In [4]:
path_data = '../data/raw/'

feat_date = "Date"

1.3 Datasets¶

In [5]:
df = pd.read_csv(os.path.join(path_data, "dataset.gz"), sep=";")
df.sample(5)
Out[5]:
Date Fourni Ventes id
67459 2022-08-11 8.0 8.0 -8793000144743990337
48519 2021-10-27 7.0 4.0 2942031581314612067
44782 2021-09-03 10.0 6.0 6183672341764466493
78372 2023-01-30 10.0 5.0 1129067540243158717
71371 2022-10-14 7.0 3.0 -1139387161625682197
In [6]:
df = set_type(df, feat_date=feat_date)

2. ACF & PACF¶

In [7]:
df_analyze = df[(df["Ventes"].notnull())].groupby([feat_date, "id"])["Ventes"].mean().reset_index().set_index(feat_date)
for num, i in enumerate(df_analyze["id"].unique()):
    try:
        fig, ax = plt.subplots(1,2,figsize=(15,3))
        temp = df_analyze[(df_analyze["id"] == i)]
        sm.graphics.tsa.plot_acf(temp["Ventes"], lags=150, ax=ax[0], title = "AUTOCORRELATION\n" + i)
        sm.graphics.tsa.plot_pacf(temp["Ventes"], lags=150, ax=ax[1], title = "PARTIAL AUTOCORRELATION\n" + i)
    except:
        pass

/var/folders/q1/n9jt1sqj3976rqf2zn88_qpc0000gn/T/ipykernel_87722/25242624.py:4: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  fig, ax = plt.subplots(1,2,figsize=(15,3))

Focus on the PACF for the top seller in order to have a clearer view

In [8]:
top_id = "-8295888135039322961"

df_analyze_id = df[(df["Ventes"].notnull()) & (df["id"] == top_id)].groupby([feat_date])["Ventes"].mean().reset_index().set_index(feat_date)
fig, ax = plt.subplots(figsize=(15,5))
sm.graphics.tsa.plot_acf(df_analyze_id["Ventes"], lags=150, ax=ax, title = "AUTOCORRELATION\n" + top_id)
plt.show()

fig, ax = plt.subplots(figsize=(15,5))
sm.graphics.tsa.plot_pacf(temp["Ventes"], lags=150, ax=ax, title = "PARTIAL AUTOCORRELATION\n" + top_id)
plt.show()

Looking at the PACF plot, select the following lags to try for the model: 1, 2, 3, 4, 5, 6, 7, 10, 13, 15, 34

As we consider we should decide two weeks before, our lag should start at least 14.

In [9]:
lags_to_try = [1, 2, 3, 4, 5, 6, 7, 10, 13, 15, 34]

5. Split the dataset into train and test¶

For the split, we choose to leave one week as we consider it might be the time needed to prepare everything (order ressources to print, order the right amount for delivery, etc.). Of course, this choice is arbitrary and might need to be changed depending on the customer.

In [10]:
# Relod dataset
df = pd.read_csv(os.path.join(path_data, "dataset.gz"), sep=";")

# Set type
df = set_type(df, feat_date="Date")
# Add lags seen
df = add_lags_sma(df, lags_to_try, feat_id='id', feat_date=feat_date)
# Split into train and test
df_train, df_test, y_train, y_test = split_train_test(df=df, feat_date="Date")
check_split(df_train, df_test, feat_date)

# Save
path_save = '../data/processed'
df_train.to_pickle(os.path.join(path_save, "train.pkl"))
df_test.to_pickle(os.path.join(path_save, "test.pkl"))
y_train.to_csv(os.path.join(path_save, "target_train.csv"))
y_test.to_csv(os.path.join(path_save, "target_test.csv"))

Train: 2020-01-02 00:00:00 2023-04-02 00:00:00
Test: 2023-04-03 00:00:00 2023-04-17 00:00:00
Ind in both train and test: []

/Users/benjaminwallyn/Documents/Git/demand-forecast/notebooks/../src/prepare_data.py:68: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_train.drop(columns="Ventes", inplace=True)
/Users/benjaminwallyn/Documents/Git/demand-forecast/notebooks/../src/prepare_data.py:69: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_test.drop(columns="Ventes", inplace=True)
In [11]:
df
Out[11]:
Date Fourni Ventes id sma_1_ventes_lag14 sma_2_ventes_lag14 sma_3_ventes_lag14 sma_4_ventes_lag14 sma_5_ventes_lag14 sma_6_ventes_lag14 sma_7_ventes_lag14 sma_10_ventes_lag14 sma_13_ventes_lag14 sma_15_ventes_lag14 sma_34_ventes_lag14
0 2020-01-02 10.0 5.0 -478139654568867546 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
72 2020-01-02 7.0 2.0 -876129535061941331 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
71 2020-01-02 6.0 3.0 4324517785573311838 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
70 2020-01-02 2.0 2.0 5056562842583071429 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
69 2020-01-02 15.0 8.0 3525884501202641829 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
83316 2023-04-17 5.0 5.0 294028829836586752 1.0 1.0 1.000000 1.0 1.4 1.166667 1.000000 0.9 1.000000 1.066667 1.235294
83317 2023-04-17 21.0 21.0 6858783929733742556 21.0 25.5 33.333333 30.0 26.8 25.333333 24.000000 25.6 23.538462 22.533333 21.882353
83318 2023-04-17 2.0 2.0 2132453013379446212 1.0 1.0 1.333333 1.0 1.2 1.333333 1.428571 1.5 1.461538 1.466667 1.500000
83305 2023-04-17 10.0 10.0 -8817594176277759455 4.0 6.5 6.333333 6.0 6.0 7.166667 7.285714 7.1 7.307692 7.133333 6.823529
83350 2023-04-17 10.0 10.0 1129067540243158717 0.0 0.0 0.000000 1.0 2.0 3.333333 3.428571 3.2 3.923077 4.266667 5.941176

83351 rows × 15 columns

In [ ]: